Recently, neural implicit functions have demonstrated remarkable results in the field of multi-view reconstruction. However, most existing methods are tailored for dense views and exhibit unsatisfactory performance when dealing with sparse views. Several latest methods have been proposed for generalizing implicit reconstruction to address the sparse view reconstruction task, but they still suffer from high training costs and are merely valid under carefully selected perspectives. In this paper, we propose a novel sparse view reconstruction framework that leverages on-surface priors to achieve highly faithful surface reconstruction. Specifically, we design several constraints on global geometry alignment and local geometry refinement for jointly optimizing coarse shapes and fine details. To achieve this, we train a neural network to learn a global implicit field from the on-surface points obtained from SfM and then leverage it as a coarse geometric constraint. To exploit local geometric consistency, we project on-surface points onto seen and unseen views, treating the consistent loss of projected features as a fine geometric constraint. The experimental results with DTU and BlendedMVS datasets in two prevalent sparse settings demonstrate significant improvements over the state-of-the-art methods.

翻译：近日，神经隐式函数在多视角重建领域取得了显著成果。然而，现有方法大多针对密集视角设计，在处理稀疏视角时表现欠佳。近年来提出的几种泛化隐式重建方法虽然尝试解决稀疏视角重建任务，但仍存在训练成本高、仅适用于特定精心选择视角的问题。本文提出一种新颖的稀疏视角重建框架，通过利用表面先验实现高保真表面重建。具体而言，我们设计了全局几何对齐与局部几何细化约束，以联合优化粗糙形状与精细细节。为此，我们训练神经网络从SfM（运动恢复结构）获取的表面点中学习全局隐式场，并将其作为粗糙几何约束；同时，通过将表面点投影至可见与不可见视角，利用投影特征的一致性损失作为精细几何约束，从而挖掘局部几何一致性。在DTU与BlendedMVS数据集上针对两种典型稀疏设置进行的实验表明，本方法相较于现有最优技术取得了显著提升。